* using log directory 'd:/Rcompile/CRANpkg/local/3.3/mlr.Rcheck' * using R version 3.3.3 (2017-03-06) * using platform: x86_64-w64-mingw32 (64-bit) * using session charset: ISO8859-1 * checking for file 'mlr/DESCRIPTION' ... OK * this is package 'mlr' version '2.12.1' * package encoding: UTF-8 * checking package namespace information ... OK * checking package dependencies ... OK * checking if this is a source package ... OK * checking if there is a namespace ... OK * checking for hidden files and directories ... OK * checking for portable file names ... OK * checking whether package 'mlr' can be installed ... OK * checking installed package size ... OK * checking package directory ... OK * checking 'build' directory ... OK * checking DESCRIPTION meta-information ... OK * checking top-level files ... OK * checking for left-over files ... OK * checking index information ... OK * checking package subdirectories ... OK * checking R files for non-ASCII characters ... OK * checking R files for syntax errors ... OK * loading checks for arch 'i386' ** checking whether the package can be loaded ... OK ** checking whether the package can be loaded with stated dependencies ... OK ** checking whether the package can be unloaded cleanly ... OK ** checking whether the namespace can be loaded with stated dependencies ... OK ** checking whether the namespace can be unloaded cleanly ... OK ** checking loading without being on the library search path ... OK ** checking use of S3 registration ... OK * loading checks for arch 'x64' ** checking whether the package can be loaded ... OK ** checking whether the package can be loaded with stated dependencies ... OK ** checking whether the package can be unloaded cleanly ... OK ** checking whether the namespace can be loaded with stated dependencies ... OK ** checking whether the namespace can be unloaded cleanly ... OK ** checking loading without being on the library search path ... OK ** checking use of S3 registration ... OK * checking dependencies in R code ... OK * checking S3 generic/method consistency ... OK * checking replacement functions ... OK * checking foreign function calls ... OK * checking R code for possible problems ... OK * checking Rd files ... OK * checking Rd metadata ... OK * checking Rd cross-references ... OK * checking for missing documentation entries ... OK * checking for code/documentation mismatches ... OK * checking Rd \usage sections ... OK * checking Rd contents ... OK * checking for unstated dependencies in examples ... OK * checking contents of 'data' directory ... OK * checking data for non-ASCII characters ... OK * checking data for ASCII and uncompressed saves ... OK * checking line endings in C/C++/Fortran sources/headers ... OK * checking compiled code ... OK * checking installed files from 'inst/doc' ... OK * checking files in 'vignettes' ... OK * checking examples ... ** running examples for arch 'i386' ... [34s] OK ** running examples for arch 'x64' ... [37s] OK * checking for unstated dependencies in 'tests' ... OK * checking tests ... ** running tests for arch 'i386' ... [549s] ERROR Running 'run-base.R' [548s] Running the tests in 'tests/run-base.R' failed. Complete output: > library(testthat) > test_check("mlr", filter = "base_") Loading required package: mlr Loading required package: ParamHelpers Attaching package: 'rex' The following object is masked from 'package:testthat': matches -- 1. Error: getHyperPars (@test_base_getHyperPars.R#19) ---------------------- For learner multilabel.rFerns please install the following packages: rFerns 1: makeLearner("multilabel.rFerns") at testthat/test_base_getHyperPars.R:19 2: do.call(constructor, list()) 3: (function () { makeRLearnerMultilabel(cl = "multilabel.rFerns", package = "rFerns", par.set = makeParamSet(makeIntegerLearnerParam(id = "depth", default = 5L), makeIntegerLearnerParam(id = "ferns", default = 1000L)), properties = c("numerics", "factors", "ordered"), name = "Random ferns", short.name = "rFerns", callees = "rFerns") })() 4: makeRLearnerMultilabel(cl = "multilabel.rFerns", package = "rFerns", par.set = makeParamSet(makeIntegerLearnerParam(id = "depth", default = 5L), makeIntegerLearnerParam(id = "ferns", default = 1000L)), properties = c("numerics", "factors", "ordered"), name = "Random ferns", short.name = "rFerns", callees = "rFerns") 5: addClasses(makeRLearnerInternal(cl, "multilabel", package, par.set, par.vals, properties, name, short.name, note, callees), c(cl, "RLearnerMultilabel")) 6: makeRLearnerInternal(cl, "multilabel", package, par.set, par.vals, properties, name, short.name, note, callees) 7: requirePackages(package, why = stri_paste("learner", id, sep = " "), default.method = "load") 8: stopf("For %s please install the following packages: %s", why, ps) -- 2. Error: WeightedClassesWrapper, binary (@test_base_imbal_weightedclasses.R# For learner classif.LiblineaRL1L2SVC please install the following packages: LiblineaR 1: lapply(learners, function(lrn) { cm1 = f(lrn, 0.001) cm2 = f(lrn, 1) cm3 = f(lrn, 1000) expect_true(all(cm1[, pos] <= cm2[, pos])) expect_true(all(cm2[, pos] <= cm3[, pos])) }) at testthat/test_base_imbal_weightedclasses.R:16 2: FUN(X[[i]], ...) 3: f(lrn, 0.001) at testthat/test_base_imbal_weightedclasses.R:17 4: makeLearner(lrn) at testthat/test_base_imbal_weightedclasses.R:6 5: do.call(constructor, list()) 6: (function () { makeRLearnerClassif(cl = "classif.LiblineaRL1L2SVC", package = "LiblineaR", par.set = makeParamSet(makeNumericLearnerParam(id = "cost", default = 1, lower = 0), makeNumericLearnerParam(id = "epsilon", default = 0.01, lower = 0), makeLogicalLearnerParam(id = "bias", default = TRUE), makeNumericVectorLearnerParam(id = "wi", len = NA_integer_), makeIntegerLearnerParam(id = "cross", default = 0L, lower = 0L, tunable = FALSE), makeLogicalLearnerParam(id = "verbose", default = FALSE, tunable = FALSE)), properties = c("twoclass", "multiclass", "numerics", "class.weights"), class.weights.param = "wi", name = "L1-Regularized L2-Loss Support Vector Classification", short.name = "liblinl1l2svc", callees = "LiblineaR") })() 7: makeRLearnerClassif(cl = "classif.LiblineaRL1L2SVC", package = "LiblineaR", par.set = makeParamSet(makeNumericLearnerParam(id = "cost", default = 1, lower = 0), makeNumericLearnerParam(id = "epsilon", default = 0.01, lower = 0), makeLogicalLearnerParam(id = "bias", default = TRUE), makeNumericVectorLearnerParam(id = "wi", len = NA_integer_), makeIntegerLearnerParam(id = "cross", default = 0L, lower = 0L, tunable = FALSE), makeLogicalLearnerParam(id = "verbose", default = FALSE, tunable = FALSE)), properties = c("twoclass", "multiclass", "numerics", "class.weights"), class.weights.param = "wi", name = "L1-Regularized L2-Loss Support Vector Classification", short.name = "liblinl1l2svc", callees = "LiblineaR") 8: addClasses(makeRLearnerInternal(cl, "classif", package, par.set, par.vals, properties, name, short.name, note, callees), c(cl, "RLearnerClassif")) 9: makeRLearnerInternal(cl, "classif", package, par.set, par.vals, properties, name, short.name, note, callees) 10: requirePackages(package, why = stri_paste("learner", id, sep = " "), default.method = "load") 11: stopf("For %s please install the following packages: %s", why, ps) -- 3. Error: WeightedClassesWrapper, multiclass (@test_base_imbal_weightedclasse For learner classif.LiblineaRL1L2SVC please install the following packages: LiblineaR 1: lapply(learners, function(lrn) { classes = getTaskFactorLevels(multiclass.task)[[multiclass.target]] n = length(classes) cm1 = f(lrn, setNames(object = c(10000, 1, 1), classes)) cm2 = f(lrn, setNames(object = c(1, 10000, 1), classes)) cm3 = f(lrn, setNames(object = c(1, 1, 10000), classes)) expect_true(all(cm1[, levs[1]] >= cm2[, levs[1]])) expect_true(all(cm1[, levs[1]] >= cm3[, levs[1]])) expect_true(all(cm2[, levs[2]] >= cm1[, levs[2]])) expect_true(all(cm2[, levs[2]] >= cm3[, levs[2]])) expect_true(all(cm3[, levs[3]] >= cm1[, levs[3]])) expect_true(all(cm3[, levs[3]] >= cm2[, levs[3]])) }) at testthat/test_base_imbal_weightedclasses.R:41 2: FUN(X[[i]], ...) 3: f(lrn, setNames(object = c(10000, 1, 1), classes)) at testthat/test_base_imbal_weightedclasses.R:44 4: makeLearner(lrn) at testthat/test_base_imbal_weightedclasses.R:31 5: do.call(constructor, list()) 6: (function () { makeRLearnerClassif(cl = "classif.LiblineaRL1L2SVC", package = "LiblineaR", par.set = makeParamSet(makeNumericLearnerParam(id = "cost", default = 1, lower = 0), makeNumericLearnerParam(id = "epsilon", default = 0.01, lower = 0), makeLogicalLearnerParam(id = "bias", default = TRUE), makeNumericVectorLearnerParam(id = "wi", len = NA_integer_), makeIntegerLearnerParam(id = "cross", default = 0L, lower = 0L, tunable = FALSE), makeLogicalLearnerParam(id = "verbose", default = FALSE, tunable = FALSE)), properties = c("twoclass", "multiclass", "numerics", "class.weights"), class.weights.param = "wi", name = "L1-Regularized L2-Loss Support Vector Classification", short.name = "liblinl1l2svc", callees = "LiblineaR") })() 7: makeRLearnerClassif(cl = "classif.LiblineaRL1L2SVC", package = "LiblineaR", par.set = makeParamSet(makeNumericLearnerParam(id = "cost", default = 1, lower = 0), makeNumericLearnerParam(id = "epsilon", default = 0.01, lower = 0), makeLogicalLearnerParam(id = "bias", default = TRUE), makeNumericVectorLearnerParam(id = "wi", len = NA_integer_), makeIntegerLearnerParam(id = "cross", default = 0L, lower = 0L, tunable = FALSE), makeLogicalLearnerParam(id = "verbose", default = FALSE, tunable = FALSE)), properties = c("twoclass", "multiclass", "numerics", "class.weights"), class.weights.param = "wi", name = "L1-Regularized L2-Loss Support Vector Classification", short.name = "liblinl1l2svc", callees = "LiblineaR") 8: addClasses(makeRLearnerInternal(cl, "classif", package, par.set, par.vals, properties, name, short.name, note, callees), c(cl, "RLearnerClassif")) 9: makeRLearnerInternal(cl, "classif", package, par.set, par.vals, properties, name, short.name, note, callees) 10: requirePackages(package, why = stri_paste("learner", id, sep = " "), default.method = "load") 11: stopf("For %s please install the following packages: %s", why, ps) -- 4. Error: getClassWeightParam (@test_base_imbal_weightedclasses.R#72) ------ For learner classif.LiblineaRL1L2SVC please install the following packages: LiblineaR 1: lapply(learners, f) at testthat/test_base_imbal_weightedclasses.R:72 2: FUN(X[[i]], ...) 3: makeLearner(lrn) at testthat/test_base_imbal_weightedclasses.R:64 4: do.call(constructor, list()) 5: (function () { makeRLearnerClassif(cl = "classif.LiblineaRL1L2SVC", package = "LiblineaR", par.set = makeParamSet(makeNumericLearnerParam(id = "cost", default = 1, lower = 0), makeNumericLearnerParam(id = "epsilon", default = 0.01, lower = 0), makeLogicalLearnerParam(id = "bias", default = TRUE), makeNumericVectorLearnerParam(id = "wi", len = NA_integer_), makeIntegerLearnerParam(id = "cross", default = 0L, lower = 0L, tunable = FALSE), makeLogicalLearnerParam(id = "verbose", default = FALSE, tunable = FALSE)), properties = c("twoclass", "multiclass", "numerics", "class.weights"), class.weights.param = "wi", name = "L1-Regularized L2-Loss Support Vector Classification", short.name = "liblinl1l2svc", callees = "LiblineaR") })() 6: makeRLearnerClassif(cl = "classif.LiblineaRL1L2SVC", package = "LiblineaR", par.set = makeParamSet(makeNumericLearnerParam(id = "cost", default = 1, lower = 0), makeNumericLearnerParam(id = "epsilon", default = 0.01, lower = 0), makeLogicalLearnerParam(id = "bias", default = TRUE), makeNumericVectorLearnerParam(id = "wi", len = NA_integer_), makeIntegerLearnerParam(id = "cross", default = 0L, lower = 0L, tunable = FALSE), makeLogicalLearnerParam(id = "verbose", default = FALSE, tunable = FALSE)), properties = c("twoclass", "multiclass", "numerics", "class.weights"), class.weights.param = "wi", name = "L1-Regularized L2-Loss Support Vector Classification", short.name = "liblinl1l2svc", callees = "LiblineaR") 7: addClasses(makeRLearnerInternal(cl, "classif", package, par.set, par.vals, properties, name, short.name, note, callees), c(cl, "RLearnerClassif")) 8: makeRLearnerInternal(cl, "classif", package, par.set, par.vals, properties, name, short.name, note, callees) 9: requirePackages(package, why = stri_paste("learner", id, sep = " "), default.method = "load") 10: stopf("For %s please install the following packages: %s", why, ps) -- 5. Error: measures (@test_base_measures.R#53) ------------------------------ Please install the following packages: Hmisc 1: performance(pred, model = mod, task = surv.task, measures = ms) at testthat/test_base_measures.R:53 2: vnapply(measures, doPerformanceIteration, pred = pred, task = task, model = model, td = NULL, feats = feats) 3: vapply(X = x, FUN = fun, ..., FUN.VALUE = NA_real_, USE.NAMES = use.names) 4: FUN(X[[i]], ...) 5: measure$fun(task, model, pred, feats, m$extra.args) 6: requirePackages("_Hmisc") 7: stopf("Please install the following packages: %s", ps) -- 6. Error: check measure calculations (@test_base_measures.R#171) ----------- For learner multilabel.rFerns please install the following packages: rFerns 1: makeLearner("multilabel.rFerns") at testthat/test_base_measures.R:171 2: do.call(constructor, list()) 3: (function () { makeRLearnerMultilabel(cl = "multilabel.rFerns", package = "rFerns", par.set = makeParamSet(makeIntegerLearnerParam(id = "depth", default = 5L), makeIntegerLearnerParam(id = "ferns", default = 1000L)), properties = c("numerics", "factors", "ordered"), name = "Random ferns", short.name = "rFerns", callees = "rFerns") })() 4: makeRLearnerMultilabel(cl = "multilabel.rFerns", package = "rFerns", par.set = makeParamSet(makeIntegerLearnerParam(id = "depth", default = 5L), makeIntegerLearnerParam(id = "ferns", default = 1000L)), properties = c("numerics", "factors", "ordered"), name = "Random ferns", short.name = "rFerns", callees = "rFerns") 5: addClasses(makeRLearnerInternal(cl, "multilabel", package, par.set, par.vals, properties, name, short.name, note, callees), c(cl, "RLearnerMultilabel")) 6: makeRLearnerInternal(cl, "multilabel", package, par.set, par.vals, properties, name, short.name, note, callees) 7: requirePackages(package, why = stri_paste("learner", id, sep = " "), default.method = "load") 8: stopf("For %s please install the following packages: %s", why, ps) -- 7. Error: multilabel learning (@test_base_multilabel.R#15) ----------------- For learner multilabel.rFerns please install the following packages: rFerns 1: makeLearner("multilabel.rFerns") at testthat/test_base_multilabel.R:15 2: do.call(constructor, list()) 3: (function () { makeRLearnerMultilabel(cl = "multilabel.rFerns", package = "rFerns", par.set = makeParamSet(makeIntegerLearnerParam(id = "depth", default = 5L), makeIntegerLearnerParam(id = "ferns", default = 1000L)), properties = c("numerics", "factors", "ordered"), name = "Random ferns", short.name = "rFerns", callees = "rFerns") })() 4: makeRLearnerMultilabel(cl = "multilabel.rFerns", package = "rFerns", par.set = makeParamSet(makeIntegerLearnerParam(id = "depth", default = 5L), makeIntegerLearnerParam(id = "ferns", default = 1000L)), properties = c("numerics", "factors", "ordered"), name = "Random ferns", short.name = "rFerns", callees = "rFerns") 5: addClasses(makeRLearnerInternal(cl, "multilabel", package, par.set, par.vals, properties, name, short.name, note, callees), c(cl, "RLearnerMultilabel")) 6: makeRLearnerInternal(cl, "multilabel", package, par.set, par.vals, properties, name, short.name, note, callees) 7: requirePackages(package, why = stri_paste("learner", id, sep = " "), default.method = "load") 8: stopf("For %s please install the following packages: %s", why, ps) Error in inDL(x, as.logical(local), as.logical(now), ...) : unable to load shared object 'D:/temp/RtmpemAQcP/RLIBS_229445aff5fe8/kknn/libs/i386/kknn.dll': `maximal number of DLLs reached... -- 8. Error: predict preserves rownames (@test_base_predict.R#92) ------------- For learner classif.kknn please install the following packages: kknn 1: train("classif.kknn", task = task) at testthat/test_base_predict.R:92 2: checkLearner(learner) 3: makeLearner(learner) 4: do.call(constructor, list()) 5: (function () { makeRLearnerClassif(cl = "classif.kknn", package = "!kknn", par.set = makeParamSet(makeIntegerLearnerParam(id = "k", default = 7L, lower = 1L), makeNumericLearnerParam(id = "distance", default = 2, lower = 0), makeDiscreteLearnerParam(id = "kernel", default = "optimal", values = list("rectangular", "triangular", "epanechnikov", "biweight", "triweight", "cos", "inv", "gaussian", "optimal")), makeLogicalLearnerParam(id = "scale", default = TRUE)), properties = c("twoclass", "multiclass", "numerics", "factors", "prob"), name = "k-Nearest Neighbor", short.name = "kknn", callees = "kknn") })() 6: makeRLearnerClassif(cl = "classif.kknn", package = "!kknn", par.set = makeParamSet(makeIntegerLearnerParam(id = "k", default = 7L, lower = 1L), makeNumericLearnerParam(id = "distance", default = 2, lower = 0), makeDiscreteLearnerParam(id = "kernel", default = "optimal", values = list("rectangular", "triangular", "epanechnikov", "biweight", "triweight", "cos", "inv", "gaussian", "optimal")), makeLogicalLearnerParam(id = "scale", default = TRUE)), properties = c("twoclass", "multiclass", "numerics", "factors", "prob"), name = "k-Nearest Neighbor", short.name = "kknn", callees = "kknn") 7: addClasses(makeRLearnerInternal(cl, "classif", package, par.set, par.vals, properties, name, short.name, note, callees), c(cl, "RLearnerClassif")) 8: makeRLearnerInternal(cl, "classif", package, par.set, par.vals, properties, name, short.name, note, callees) 9: requirePackages(package, why = stri_paste("learner", id, sep = " "), default.method = "load") 10: stopf("For %s please install the following packages: %s", why, ps) -- 9. Error: (unknown) (@test_base_prediction_operators.R#5) ------------------ For learner multilabel.rFerns please install the following packages: rFerns 1: predict(train("multilabel.rFerns", multilabel.task), multilabel.task) at testthat/test_base_prediction_operators.R:5 2: train("multilabel.rFerns", multilabel.task) 3: checkLearner(learner) 4: makeLearner(learner) 5: do.call(constructor, list()) 6: (function () { makeRLearnerMultilabel(cl = "multilabel.rFerns", package = "rFerns", par.set = makeParamSet(makeIntegerLearnerParam(id = "depth", default = 5L), makeIntegerLearnerParam(id = "ferns", default = 1000L)), properties = c("numerics", "factors", "ordered"), name = "Random ferns", short.name = "rFerns", callees = "rFerns") })() 7: makeRLearnerMultilabel(cl = "multilabel.rFerns", package = "rFerns", par.set = makeParamSet(makeIntegerLearnerParam(id = "depth", default = 5L), makeIntegerLearnerParam(id = "ferns", default = 1000L)), properties = c("numerics", "factors", "ordered"), name = "Random ferns", short.name = "rFerns", callees = "rFerns") 8: addClasses(makeRLearnerInternal(cl, "multilabel", package, par.set, par.vals, properties, name, short.name, note, callees), c(cl, "RLearnerMultilabel")) 9: makeRLearnerInternal(cl, "multilabel", package, par.set, par.vals, properties, name, short.name, note, callees) 10: requirePackages(package, why = stri_paste("learner", id, sep = " "), default.method = "load") 11: stopf("For %s please install the following packages: %s", why, ps) -- 10. Error: selectFeatures/sfs works with wrapper (@test_base_selectFeatures.R For learner classif.LiblineaRL2LogReg please install the following packages: LiblineaR 1: makeLearner("classif.LiblineaRL2LogReg") at testthat/test_base_selectFeatures.R:64 2: do.call(constructor, list()) 3: (function () { makeRLearnerClassif(cl = "classif.LiblineaRL2LogReg", package = "LiblineaR", par.set = makeParamSet(makeDiscreteLearnerParam(id = "type", default = 0L, values = c(0L, 7L)), makeNumericLearnerParam(id = "cost", default = 1, lower = 0), makeNumericLearnerParam(id = "epsilon", lower = 0), makeLogicalLearnerParam(id = "bias", default = TRUE), makeNumericVectorLearnerParam(id = "wi", len = NA_integer_), makeIntegerLearnerParam(id = "cross", default = 0L, lower = 0L, tunable = FALSE), makeLogicalLearnerParam(id = "verbose", default = FALSE, tunable = FALSE)), par.vals = list(type = 0L), properties = c("twoclass", "multiclass", "numerics", "class.weights", "prob"), class.weights.param = "wi", name = "L2-Regularized Logistic Regression", short.name = "liblinl2logreg", note = "`type = 0` (the default) is primal and `type = 7` is dual problem.", callees = "LiblineaR") })() 4: makeRLearnerClassif(cl = "classif.LiblineaRL2LogReg", package = "LiblineaR", par.set = makeParamSet(makeDiscreteLearnerParam(id = "type", default = 0L, values = c(0L, 7L)), makeNumericLearnerParam(id = "cost", default = 1, lower = 0), makeNumericLearnerParam(id = "epsilon", lower = 0), makeLogicalLearnerParam(id = "bias", default = TRUE), makeNumericVectorLearnerParam(id = "wi", len = NA_integer_), makeIntegerLearnerParam(id = "cross", default = 0L, lower = 0L, tunable = FALSE), makeLogicalLearnerParam(id = "verbose", default = FALSE, tunable = FALSE)), par.vals = list(type = 0L), properties = c("twoclass", "multiclass", "numerics", "class.weights", "prob"), class.weights.param = "wi", name = "L2-Regularized Logistic Regression", short.name = "liblinl2logreg", note = "`type = 0` (the default) is primal and `type = 7` is dual problem.", callees = "LiblineaR") 5: addClasses(makeRLearnerInternal(cl, "classif", package, par.set, par.vals, properties, name, short.name, note, callees), c(cl, "RLearnerClassif")) 6: makeRLearnerInternal(cl, "classif", package, par.set, par.vals, properties, name, short.name, note, callees) 7: requirePackages(package, why = stri_paste("learner", id, sep = " "), default.method = "load") 8: stopf("For %s please install the following packages: %s", why, ps) == testthat results =========================================================== OK: 3232 SKIPPED: 0 FAILED: 10 1. Error: getHyperPars (@test_base_getHyperPars.R#19) 2. Error: WeightedClassesWrapper, binary (@test_base_imbal_weightedclasses.R#16) 3. Error: WeightedClassesWrapper, multiclass (@test_base_imbal_weightedclasses.R#41) 4. Error: getClassWeightParam (@test_base_imbal_weightedclasses.R#72) 5. Error: measures (@test_base_measures.R#53) 6. Error: check measure calculations (@test_base_measures.R#171) 7. Error: multilabel learning (@test_base_multilabel.R#15) 8. Error: predict preserves rownames (@test_base_predict.R#92) 9. Error: (unknown) (@test_base_prediction_operators.R#5) 10. Error: selectFeatures/sfs works with wrapper (@test_base_selectFeatures.R#64) Error: testthat unit tests failed Execution halted ** running tests for arch 'x64' ... [557s] ERROR Running 'run-base.R' [556s] Running the tests in 'tests/run-base.R' failed. Complete output: > library(testthat) > test_check("mlr", filter = "base_") Loading required package: mlr Loading required package: ParamHelpers Attaching package: 'rex' The following object is masked from 'package:testthat': matches -- 1. Error: getHyperPars (@test_base_getHyperPars.R#19) ---------------------- For learner multilabel.rFerns please install the following packages: rFerns 1: makeLearner("multilabel.rFerns") at testthat/test_base_getHyperPars.R:19 2: do.call(constructor, list()) 3: (function () { makeRLearnerMultilabel(cl = "multilabel.rFerns", package = "rFerns", par.set = makeParamSet(makeIntegerLearnerParam(id = "depth", default = 5L), makeIntegerLearnerParam(id = "ferns", default = 1000L)), properties = c("numerics", "factors", "ordered"), name = "Random ferns", short.name = "rFerns", callees = "rFerns") })() 4: makeRLearnerMultilabel(cl = "multilabel.rFerns", package = "rFerns", par.set = makeParamSet(makeIntegerLearnerParam(id = "depth", default = 5L), makeIntegerLearnerParam(id = "ferns", default = 1000L)), properties = c("numerics", "factors", "ordered"), name = "Random ferns", short.name = "rFerns", callees = "rFerns") 5: addClasses(makeRLearnerInternal(cl, "multilabel", package, par.set, par.vals, properties, name, short.name, note, callees), c(cl, "RLearnerMultilabel")) 6: makeRLearnerInternal(cl, "multilabel", package, par.set, par.vals, properties, name, short.name, note, callees) 7: requirePackages(package, why = stri_paste("learner", id, sep = " "), default.method = "load") 8: stopf("For %s please install the following packages: %s", why, ps) -- 2. Error: WeightedClassesWrapper, binary (@test_base_imbal_weightedclasses.R# For learner classif.LiblineaRL1L2SVC please install the following packages: LiblineaR 1: lapply(learners, function(lrn) { cm1 = f(lrn, 0.001) cm2 = f(lrn, 1) cm3 = f(lrn, 1000) expect_true(all(cm1[, pos] <= cm2[, pos])) expect_true(all(cm2[, pos] <= cm3[, pos])) }) at testthat/test_base_imbal_weightedclasses.R:16 2: FUN(X[[i]], ...) 3: f(lrn, 0.001) at testthat/test_base_imbal_weightedclasses.R:17 4: makeLearner(lrn) at testthat/test_base_imbal_weightedclasses.R:6 5: do.call(constructor, list()) 6: (function () { makeRLearnerClassif(cl = "classif.LiblineaRL1L2SVC", package = "LiblineaR", par.set = makeParamSet(makeNumericLearnerParam(id = "cost", default = 1, lower = 0), makeNumericLearnerParam(id = "epsilon", default = 0.01, lower = 0), makeLogicalLearnerParam(id = "bias", default = TRUE), makeNumericVectorLearnerParam(id = "wi", len = NA_integer_), makeIntegerLearnerParam(id = "cross", default = 0L, lower = 0L, tunable = FALSE), makeLogicalLearnerParam(id = "verbose", default = FALSE, tunable = FALSE)), properties = c("twoclass", "multiclass", "numerics", "class.weights"), class.weights.param = "wi", name = "L1-Regularized L2-Loss Support Vector Classification", short.name = "liblinl1l2svc", callees = "LiblineaR") })() 7: makeRLearnerClassif(cl = "classif.LiblineaRL1L2SVC", package = "LiblineaR", par.set = makeParamSet(makeNumericLearnerParam(id = "cost", default = 1, lower = 0), makeNumericLearnerParam(id = "epsilon", default = 0.01, lower = 0), makeLogicalLearnerParam(id = "bias", default = TRUE), makeNumericVectorLearnerParam(id = "wi", len = NA_integer_), makeIntegerLearnerParam(id = "cross", default = 0L, lower = 0L, tunable = FALSE), makeLogicalLearnerParam(id = "verbose", default = FALSE, tunable = FALSE)), properties = c("twoclass", "multiclass", "numerics", "class.weights"), class.weights.param = "wi", name = "L1-Regularized L2-Loss Support Vector Classification", short.name = "liblinl1l2svc", callees = "LiblineaR") 8: addClasses(makeRLearnerInternal(cl, "classif", package, par.set, par.vals, properties, name, short.name, note, callees), c(cl, "RLearnerClassif")) 9: makeRLearnerInternal(cl, "classif", package, par.set, par.vals, properties, name, short.name, note, callees) 10: requirePackages(package, why = stri_paste("learner", id, sep = " "), default.method = "load") 11: stopf("For %s please install the following packages: %s", why, ps) -- 3. Error: WeightedClassesWrapper, multiclass (@test_base_imbal_weightedclasse For learner classif.LiblineaRL1L2SVC please install the following packages: LiblineaR 1: lapply(learners, function(lrn) { classes = getTaskFactorLevels(multiclass.task)[[multiclass.target]] n = length(classes) cm1 = f(lrn, setNames(object = c(10000, 1, 1), classes)) cm2 = f(lrn, setNames(object = c(1, 10000, 1), classes)) cm3 = f(lrn, setNames(object = c(1, 1, 10000), classes)) expect_true(all(cm1[, levs[1]] >= cm2[, levs[1]])) expect_true(all(cm1[, levs[1]] >= cm3[, levs[1]])) expect_true(all(cm2[, levs[2]] >= cm1[, levs[2]])) expect_true(all(cm2[, levs[2]] >= cm3[, levs[2]])) expect_true(all(cm3[, levs[3]] >= cm1[, levs[3]])) expect_true(all(cm3[, levs[3]] >= cm2[, levs[3]])) }) at testthat/test_base_imbal_weightedclasses.R:41 2: FUN(X[[i]], ...) 3: f(lrn, setNames(object = c(10000, 1, 1), classes)) at testthat/test_base_imbal_weightedclasses.R:44 4: makeLearner(lrn) at testthat/test_base_imbal_weightedclasses.R:31 5: do.call(constructor, list()) 6: (function () { makeRLearnerClassif(cl = "classif.LiblineaRL1L2SVC", package = "LiblineaR", par.set = makeParamSet(makeNumericLearnerParam(id = "cost", default = 1, lower = 0), makeNumericLearnerParam(id = "epsilon", default = 0.01, lower = 0), makeLogicalLearnerParam(id = "bias", default = TRUE), makeNumericVectorLearnerParam(id = "wi", len = NA_integer_), makeIntegerLearnerParam(id = "cross", default = 0L, lower = 0L, tunable = FALSE), makeLogicalLearnerParam(id = "verbose", default = FALSE, tunable = FALSE)), properties = c("twoclass", "multiclass", "numerics", "class.weights"), class.weights.param = "wi", name = "L1-Regularized L2-Loss Support Vector Classification", short.name = "liblinl1l2svc", callees = "LiblineaR") })() 7: makeRLearnerClassif(cl = "classif.LiblineaRL1L2SVC", package = "LiblineaR", par.set = makeParamSet(makeNumericLearnerParam(id = "cost", default = 1, lower = 0), makeNumericLearnerParam(id = "epsilon", default = 0.01, lower = 0), makeLogicalLearnerParam(id = "bias", default = TRUE), makeNumericVectorLearnerParam(id = "wi", len = NA_integer_), makeIntegerLearnerParam(id = "cross", default = 0L, lower = 0L, tunable = FALSE), makeLogicalLearnerParam(id = "verbose", default = FALSE, tunable = FALSE)), properties = c("twoclass", "multiclass", "numerics", "class.weights"), class.weights.param = "wi", name = "L1-Regularized L2-Loss Support Vector Classification", short.name = "liblinl1l2svc", callees = "LiblineaR") 8: addClasses(makeRLearnerInternal(cl, "classif", package, par.set, par.vals, properties, name, short.name, note, callees), c(cl, "RLearnerClassif")) 9: makeRLearnerInternal(cl, "classif", package, par.set, par.vals, properties, name, short.name, note, callees) 10: requirePackages(package, why = stri_paste("learner", id, sep = " "), default.method = "load") 11: stopf("For %s please install the following packages: %s", why, ps) -- 4. Error: getClassWeightParam (@test_base_imbal_weightedclasses.R#72) ------ For learner classif.LiblineaRL1L2SVC please install the following packages: LiblineaR 1: lapply(learners, f) at testthat/test_base_imbal_weightedclasses.R:72 2: FUN(X[[i]], ...) 3: makeLearner(lrn) at testthat/test_base_imbal_weightedclasses.R:64 4: do.call(constructor, list()) 5: (function () { makeRLearnerClassif(cl = "classif.LiblineaRL1L2SVC", package = "LiblineaR", par.set = makeParamSet(makeNumericLearnerParam(id = "cost", default = 1, lower = 0), makeNumericLearnerParam(id = "epsilon", default = 0.01, lower = 0), makeLogicalLearnerParam(id = "bias", default = TRUE), makeNumericVectorLearnerParam(id = "wi", len = NA_integer_), makeIntegerLearnerParam(id = "cross", default = 0L, lower = 0L, tunable = FALSE), makeLogicalLearnerParam(id = "verbose", default = FALSE, tunable = FALSE)), properties = c("twoclass", "multiclass", "numerics", "class.weights"), class.weights.param = "wi", name = "L1-Regularized L2-Loss Support Vector Classification", short.name = "liblinl1l2svc", callees = "LiblineaR") })() 6: makeRLearnerClassif(cl = "classif.LiblineaRL1L2SVC", package = "LiblineaR", par.set = makeParamSet(makeNumericLearnerParam(id = "cost", default = 1, lower = 0), makeNumericLearnerParam(id = "epsilon", default = 0.01, lower = 0), makeLogicalLearnerParam(id = "bias", default = TRUE), makeNumericVectorLearnerParam(id = "wi", len = NA_integer_), makeIntegerLearnerParam(id = "cross", default = 0L, lower = 0L, tunable = FALSE), makeLogicalLearnerParam(id = "verbose", default = FALSE, tunable = FALSE)), properties = c("twoclass", "multiclass", "numerics", "class.weights"), class.weights.param = "wi", name = "L1-Regularized L2-Loss Support Vector Classification", short.name = "liblinl1l2svc", callees = "LiblineaR") 7: addClasses(makeRLearnerInternal(cl, "classif", package, par.set, par.vals, properties, name, short.name, note, callees), c(cl, "RLearnerClassif")) 8: makeRLearnerInternal(cl, "classif", package, par.set, par.vals, properties, name, short.name, note, callees) 9: requirePackages(package, why = stri_paste("learner", id, sep = " "), default.method = "load") 10: stopf("For %s please install the following packages: %s", why, ps) -- 5. Error: measures (@test_base_measures.R#53) ------------------------------ Please install the following packages: Hmisc 1: performance(pred, model = mod, task = surv.task, measures = ms) at testthat/test_base_measures.R:53 2: vnapply(measures, doPerformanceIteration, pred = pred, task = task, model = model, td = NULL, feats = feats) 3: vapply(X = x, FUN = fun, ..., FUN.VALUE = NA_real_, USE.NAMES = use.names) 4: FUN(X[[i]], ...) 5: measure$fun(task, model, pred, feats, m$extra.args) 6: requirePackages("_Hmisc") 7: stopf("Please install the following packages: %s", ps) -- 6. Error: check measure calculations (@test_base_measures.R#171) ----------- For learner multilabel.rFerns please install the following packages: rFerns 1: makeLearner("multilabel.rFerns") at testthat/test_base_measures.R:171 2: do.call(constructor, list()) 3: (function () { makeRLearnerMultilabel(cl = "multilabel.rFerns", package = "rFerns", par.set = makeParamSet(makeIntegerLearnerParam(id = "depth", default = 5L), makeIntegerLearnerParam(id = "ferns", default = 1000L)), properties = c("numerics", "factors", "ordered"), name = "Random ferns", short.name = "rFerns", callees = "rFerns") })() 4: makeRLearnerMultilabel(cl = "multilabel.rFerns", package = "rFerns", par.set = makeParamSet(makeIntegerLearnerParam(id = "depth", default = 5L), makeIntegerLearnerParam(id = "ferns", default = 1000L)), properties = c("numerics", "factors", "ordered"), name = "Random ferns", short.name = "rFerns", callees = "rFerns") 5: addClasses(makeRLearnerInternal(cl, "multilabel", package, par.set, par.vals, properties, name, short.name, note, callees), c(cl, "RLearnerMultilabel")) 6: makeRLearnerInternal(cl, "multilabel", package, par.set, par.vals, properties, name, short.name, note, callees) 7: requirePackages(package, why = stri_paste("learner", id, sep = " "), default.method = "load") 8: stopf("For %s please install the following packages: %s", why, ps) -- 7. Error: multilabel learning (@test_base_multilabel.R#15) ----------------- For learner multilabel.rFerns please install the following packages: rFerns 1: makeLearner("multilabel.rFerns") at testthat/test_base_multilabel.R:15 2: do.call(constructor, list()) 3: (function () { makeRLearnerMultilabel(cl = "multilabel.rFerns", package = "rFerns", par.set = makeParamSet(makeIntegerLearnerParam(id = "depth", default = 5L), makeIntegerLearnerParam(id = "ferns", default = 1000L)), properties = c("numerics", "factors", "ordered"), name = "Random ferns", short.name = "rFerns", callees = "rFerns") })() 4: makeRLearnerMultilabel(cl = "multilabel.rFerns", package = "rFerns", par.set = makeParamSet(makeIntegerLearnerParam(id = "depth", default = 5L), makeIntegerLearnerParam(id = "ferns", default = 1000L)), properties = c("numerics", "factors", "ordered"), name = "Random ferns", short.name = "rFerns", callees = "rFerns") 5: addClasses(makeRLearnerInternal(cl, "multilabel", package, par.set, par.vals, properties, name, short.name, note, callees), c(cl, "RLearnerMultilabel")) 6: makeRLearnerInternal(cl, "multilabel", package, par.set, par.vals, properties, name, short.name, note, callees) 7: requirePackages(package, why = stri_paste("learner", id, sep = " "), default.method = "load") 8: stopf("For %s please install the following packages: %s", why, ps) Error in inDL(x, as.logical(local), as.logical(now), ...) : unable to load shared object 'D:/temp/RtmpemAQcP/RLIBS_229445aff5fe8/kknn/libs/x64/kknn.dll': `maximal number of DLLs reached... -- 8. Error: predict preserves rownames (@test_base_predict.R#92) ------------- For learner classif.kknn please install the following packages: kknn 1: train("classif.kknn", task = task) at testthat/test_base_predict.R:92 2: checkLearner(learner) 3: makeLearner(learner) 4: do.call(constructor, list()) 5: (function () { makeRLearnerClassif(cl = "classif.kknn", package = "!kknn", par.set = makeParamSet(makeIntegerLearnerParam(id = "k", default = 7L, lower = 1L), makeNumericLearnerParam(id = "distance", default = 2, lower = 0), makeDiscreteLearnerParam(id = "kernel", default = "optimal", values = list("rectangular", "triangular", "epanechnikov", "biweight", "triweight", "cos", "inv", "gaussian", "optimal")), makeLogicalLearnerParam(id = "scale", default = TRUE)), properties = c("twoclass", "multiclass", "numerics", "factors", "prob"), name = "k-Nearest Neighbor", short.name = "kknn", callees = "kknn") })() 6: makeRLearnerClassif(cl = "classif.kknn", package = "!kknn", par.set = makeParamSet(makeIntegerLearnerParam(id = "k", default = 7L, lower = 1L), makeNumericLearnerParam(id = "distance", default = 2, lower = 0), makeDiscreteLearnerParam(id = "kernel", default = "optimal", values = list("rectangular", "triangular", "epanechnikov", "biweight", "triweight", "cos", "inv", "gaussian", "optimal")), makeLogicalLearnerParam(id = "scale", default = TRUE)), properties = c("twoclass", "multiclass", "numerics", "factors", "prob"), name = "k-Nearest Neighbor", short.name = "kknn", callees = "kknn") 7: addClasses(makeRLearnerInternal(cl, "classif", package, par.set, par.vals, properties, name, short.name, note, callees), c(cl, "RLearnerClassif")) 8: makeRLearnerInternal(cl, "classif", package, par.set, par.vals, properties, name, short.name, note, callees) 9: requirePackages(package, why = stri_paste("learner", id, sep = " "), default.method = "load") 10: stopf("For %s please install the following packages: %s", why, ps) -- 9. Error: (unknown) (@test_base_prediction_operators.R#5) ------------------ For learner multilabel.rFerns please install the following packages: rFerns 1: predict(train("multilabel.rFerns", multilabel.task), multilabel.task) at testthat/test_base_prediction_operators.R:5 2: train("multilabel.rFerns", multilabel.task) 3: checkLearner(learner) 4: makeLearner(learner) 5: do.call(constructor, list()) 6: (function () { makeRLearnerMultilabel(cl = "multilabel.rFerns", package = "rFerns", par.set = makeParamSet(makeIntegerLearnerParam(id = "depth", default = 5L), makeIntegerLearnerParam(id = "ferns", default = 1000L)), properties = c("numerics", "factors", "ordered"), name = "Random ferns", short.name = "rFerns", callees = "rFerns") })() 7: makeRLearnerMultilabel(cl = "multilabel.rFerns", package = "rFerns", par.set = makeParamSet(makeIntegerLearnerParam(id = "depth", default = 5L), makeIntegerLearnerParam(id = "ferns", default = 1000L)), properties = c("numerics", "factors", "ordered"), name = "Random ferns", short.name = "rFerns", callees = "rFerns") 8: addClasses(makeRLearnerInternal(cl, "multilabel", package, par.set, par.vals, properties, name, short.name, note, callees), c(cl, "RLearnerMultilabel")) 9: makeRLearnerInternal(cl, "multilabel", package, par.set, par.vals, properties, name, short.name, note, callees) 10: requirePackages(package, why = stri_paste("learner", id, sep = " "), default.method = "load") 11: stopf("For %s please install the following packages: %s", why, ps) -- 10. Error: selectFeatures/sfs works with wrapper (@test_base_selectFeatures.R For learner classif.LiblineaRL2LogReg please install the following packages: LiblineaR 1: makeLearner("classif.LiblineaRL2LogReg") at testthat/test_base_selectFeatures.R:64 2: do.call(constructor, list()) 3: (function () { makeRLearnerClassif(cl = "classif.LiblineaRL2LogReg", package = "LiblineaR", par.set = makeParamSet(makeDiscreteLearnerParam(id = "type", default = 0L, values = c(0L, 7L)), makeNumericLearnerParam(id = "cost", default = 1, lower = 0), makeNumericLearnerParam(id = "epsilon", lower = 0), makeLogicalLearnerParam(id = "bias", default = TRUE), makeNumericVectorLearnerParam(id = "wi", len = NA_integer_), makeIntegerLearnerParam(id = "cross", default = 0L, lower = 0L, tunable = FALSE), makeLogicalLearnerParam(id = "verbose", default = FALSE, tunable = FALSE)), par.vals = list(type = 0L), properties = c("twoclass", "multiclass", "numerics", "class.weights", "prob"), class.weights.param = "wi", name = "L2-Regularized Logistic Regression", short.name = "liblinl2logreg", note = "`type = 0` (the default) is primal and `type = 7` is dual problem.", callees = "LiblineaR") })() 4: makeRLearnerClassif(cl = "classif.LiblineaRL2LogReg", package = "LiblineaR", par.set = makeParamSet(makeDiscreteLearnerParam(id = "type", default = 0L, values = c(0L, 7L)), makeNumericLearnerParam(id = "cost", default = 1, lower = 0), makeNumericLearnerParam(id = "epsilon", lower = 0), makeLogicalLearnerParam(id = "bias", default = TRUE), makeNumericVectorLearnerParam(id = "wi", len = NA_integer_), makeIntegerLearnerParam(id = "cross", default = 0L, lower = 0L, tunable = FALSE), makeLogicalLearnerParam(id = "verbose", default = FALSE, tunable = FALSE)), par.vals = list(type = 0L), properties = c("twoclass", "multiclass", "numerics", "class.weights", "prob"), class.weights.param = "wi", name = "L2-Regularized Logistic Regression", short.name = "liblinl2logreg", note = "`type = 0` (the default) is primal and `type = 7` is dual problem.", callees = "LiblineaR") 5: addClasses(makeRLearnerInternal(cl, "classif", package, par.set, par.vals, properties, name, short.name, note, callees), c(cl, "RLearnerClassif")) 6: makeRLearnerInternal(cl, "classif", package, par.set, par.vals, properties, name, short.name, note, callees) 7: requirePackages(package, why = stri_paste("learner", id, sep = " "), default.method = "load") 8: stopf("For %s please install the following packages: %s", why, ps) == testthat results =========================================================== OK: 3232 SKIPPED: 0 FAILED: 10 1. Error: getHyperPars (@test_base_getHyperPars.R#19) 2. Error: WeightedClassesWrapper, binary (@test_base_imbal_weightedclasses.R#16) 3. Error: WeightedClassesWrapper, multiclass (@test_base_imbal_weightedclasses.R#41) 4. Error: getClassWeightParam (@test_base_imbal_weightedclasses.R#72) 5. Error: measures (@test_base_measures.R#53) 6. Error: check measure calculations (@test_base_measures.R#171) 7. Error: multilabel learning (@test_base_multilabel.R#15) 8. Error: predict preserves rownames (@test_base_predict.R#92) 9. Error: (unknown) (@test_base_prediction_operators.R#5) 10. Error: selectFeatures/sfs works with wrapper (@test_base_selectFeatures.R#64) Error: testthat unit tests failed Execution halted * checking for unstated dependencies in vignettes ... OK * checking package vignettes in 'inst/doc' ... OK * checking re-building of vignette outputs ... [6s] OK * checking PDF version of manual ... OK * DONE Status: 2 ERRORs